Introduction to scAnnotatR

Vy Nguyen

2022-04-26

Introduction

scAnnotatR is an R package for cell type prediction on single cell RNA-sequencing data. Currently, this package supports data in the forms of a Seurat object or a SingleCellExperiment object.

More information about Seurat object can be found here: https://satijalab.org/seurat/ More information about SingleCellExperiment object can be found here: https://osca.bioconductor.org/

scAnnotatR provides 2 main features:

Installation

The scAnnotatR package can be directly installed from Bioconductor:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

if (!require(scAnnotatR))
  BiocManager::install("scAnnotatR")

For more information, see https://bioconductor.org/install/.

Included models

The scAnnotatR package comes with several pre-trained models to classify cell types.

# load scAnnotatR into working space
library(scAnnotatR)
#> Loading required package: Seurat
#> Registered S3 method overwritten by 'spatstat.core':
#>   method          from
#>   formula.glmmPQL MASS
#> Attaching SeuratObject
#> Loading required package: SingleCellExperiment
#> Loading required package: SummarizedExperiment
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#> Loading required package: matrixStats
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#> The following objects are masked from 'package:matrixStats':
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#>     colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
#>     colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
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#>     rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks,
#>     rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars,
#>     rowWeightedMads, rowWeightedMeans, rowWeightedMedians,
#>     rowWeightedSds, rowWeightedVars
#> Loading required package: GenomicRanges
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#>     Vignettes contain introductory material; view with
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The models are stored in the default_models object:

default_models <- load_models("default")
#> snapshotDate(): 2022-04-21
#> loading from cache
names(default_models)
#>  [1] "B cells"           "Plasma cells"      "NK"               
#>  [4] "CD16 NK"           "CD56 NK"           "T cells"          
#>  [7] "CD4 T cells"       "CD8 T cells"       "Treg"             
#> [10] "NKT"               "ILC"               "Monocytes"        
#> [13] "CD14 Mono"         "CD16 Mono"         "DC"               
#> [16] "pDC"               "Endothelial cells" "LEC"              
#> [19] "VEC"               "Platelets"         "RBC"              
#> [22] "Melanocyte"        "Schwann cells"     "Pericytes"        
#> [25] "Mast cells"        "Keratinocytes"     "alpha"            
#> [28] "beta"              "delta"             "gamma"            
#> [31] "acinar"            "ductal"            "Fibroblasts"

The default_models object is named a list of classifiers. Each classifier is an instance of the scAnnotatR S4 class. For example:

default_models[['B cells']]
#> An object of class scAnnotatR for B cells 
#> * 31 marker genes applied: CD38, CD79B, CD74, CD84, RASGRP2, TCF3, SP140, MEF2C, DERL3, CD37, CD79A, POU2AF1, MVK, CD83, BACH2, LY86, CD86, SDC1, CR2, LRMP, VPREB3, IL2RA, BLK, IRF8, FLI1, MS4A1, CD14, MZB1, PTEN, CD19, MME 
#> * Predicting probability threshold: 0.5 
#> * No parent model

Basic pipeline to identify cell types in a scRNA-seq dataset using scAnnotatR

Preparing the data

To identify cell types available in a dataset, we need to load the dataset as Seurat or SingleCellExperiment object.

For this vignette, we use a small sample datasets that is available as a Seurat object as part of the package.

# load the example dataset
data("tirosh_mel80_example")
tirosh_mel80_example
#> An object of class Seurat 
#> 78 features across 480 samples within 1 assay 
#> Active assay: RNA (78 features, 34 variable features)
#>  1 dimensional reduction calculated: umap

The example dataset already contains the clustering results as part of the metadata. This is not necessary for the classification process.

head(tirosh_mel80_example[[]])
#>                               orig.ident nCount_RNA nFeature_RNA percent.mt
#> Cy80_II_CD45_B07_S883_comb SeuratProject   42.46011            8          0
#> Cy80_II_CD45_C09_S897_comb SeuratProject   74.35907           14          0
#> Cy80_II_CD45_H07_S955_comb SeuratProject   42.45392            8          0
#> Cy80_II_CD45_H09_S957_comb SeuratProject   63.47043           12          0
#> Cy80_II_CD45_B11_S887_comb SeuratProject   47.26798            9          0
#> Cy80_II_CD45_D11_S911_comb SeuratProject   69.12167           13          0
#>                            RNA_snn_res.0.8 seurat_clusters RNA_snn_res.0.5
#> Cy80_II_CD45_B07_S883_comb               4               4               2
#> Cy80_II_CD45_C09_S897_comb               4               4               2
#> Cy80_II_CD45_H07_S955_comb               4               4               2
#> Cy80_II_CD45_H09_S957_comb               4               4               2
#> Cy80_II_CD45_B11_S887_comb               4               4               2
#> Cy80_II_CD45_D11_S911_comb               1               1               1

Cell classification

To launch cell type identification, we simply call the classify_cells function. A detailed description of all parameters can be found through the function’s help page ?classify_cells.

Here we use only 3 classifiers for B cells, T cells and NK cells to reduce computational cost of this vignette. If users want to use all pretrained classifiers on their dataset, cell_types = 'all' can be used.

seurat.obj <- classify_cells(classify_obj = tirosh_mel80_example, 
                             assay = 'RNA', slot = 'counts',
                             cell_types = c('B cells', 'NK', 'T cells'), 
                             path_to_models = 'default')
#> snapshotDate(): 2022-04-21
#> loading from cache

Parameters

  • The option cell_types = ‘all’ tells the function to use all available cell classification models. Alternatively, we can limit the identifiable cell types:
    • by specifying: cell_types = c('B cells', 'T cells')
    • or by indicating the applicable classifier using the classifiers option: classifiers = c(default_models[['B cells']], default_models[['T cells']])
  • The option path_to_models = ‘default’ is to automatically use the package-integrated pretrained models (without loading the models into the current working space). This option can be used to load a local database instead. For more details see the vignettes on training your own classifiers.

Result interpretation

The classify_cells function returns the input object but with additional columns in the metadata table.

# display the additional metadata fields
seurat.obj[[]][c(50:60), c(8:ncol(seurat.obj[[]]))]
#>                                            B_cells_p B_cells_class      NK_p
#> cy80.Cd45.pos.PD1.pos.B09.S45.comb       0.007754246            no 0.4881285
#> cy80.Cd45.pos.Pd1.neg.S366.H06.S366.comb 0.999385770           yes 0.4440553
#> cy80.Cd45.pos.Pd1.neg.S202.A10.S202.comb 0.998317662           yes 0.4416114
#> cy80.Cd45.pos.Pd1.neg.S201.A09.S201.comb 0.997774856           yes 0.4398997
#> cy80.Cd45.pos.Pd1.neg.S221.B05.S221.comb 0.998874031           yes 0.4541005
#> cy80.Cd45.pos.PD1.pos.A03.S15.comb       0.999944282           yes 0.4511450
#> cy80.Cd45.pos.PD1.pos.B11.S47.comb       0.015978230            no 0.4841041
#> cy80.Cd45.pos.PD1.pos.S189.H09.S189.comb 0.099311534            no 0.4858084
#> cy80.Cd45.pos.PD1.pos.A05.S17.comb       0.055754074            no 0.4924746
#> cy80.Cd45.pos.PD1.pos.C02.S62.comb       0.048558881            no 0.5002238
#> cy80.Cd45.pos.PD1.pos.D12.S96.comb       0.996979702           yes 0.4994867
#>                                          NK_class  T_cells_p T_cells_class
#> cy80.Cd45.pos.PD1.pos.B09.S45.comb             no 0.94205232           yes
#> cy80.Cd45.pos.Pd1.neg.S366.H06.S366.comb       no 0.11269306            no
#> cy80.Cd45.pos.Pd1.neg.S202.A10.S202.comb       no 0.09834696            no
#> cy80.Cd45.pos.Pd1.neg.S201.A09.S201.comb       no 0.22256938            no
#> cy80.Cd45.pos.Pd1.neg.S221.B05.S221.comb       no 0.12903487            no
#> cy80.Cd45.pos.PD1.pos.A03.S15.comb             no 0.27242536            no
#> cy80.Cd45.pos.PD1.pos.B11.S47.comb             no 0.94929624           yes
#> cy80.Cd45.pos.PD1.pos.S189.H09.S189.comb       no 0.93390248           yes
#> cy80.Cd45.pos.PD1.pos.A05.S17.comb             no 0.98161289           yes
#> cy80.Cd45.pos.PD1.pos.C02.S62.comb            yes 0.96436674           yes
#> cy80.Cd45.pos.PD1.pos.D12.S96.comb             no 0.94848597           yes
#>                                          predicted_cell_type
#> cy80.Cd45.pos.PD1.pos.B09.S45.comb                   T cells
#> cy80.Cd45.pos.Pd1.neg.S366.H06.S366.comb             B cells
#> cy80.Cd45.pos.Pd1.neg.S202.A10.S202.comb             B cells
#> cy80.Cd45.pos.Pd1.neg.S201.A09.S201.comb             B cells
#> cy80.Cd45.pos.Pd1.neg.S221.B05.S221.comb             B cells
#> cy80.Cd45.pos.PD1.pos.A03.S15.comb                   B cells
#> cy80.Cd45.pos.PD1.pos.B11.S47.comb                   T cells
#> cy80.Cd45.pos.PD1.pos.S189.H09.S189.comb             T cells
#> cy80.Cd45.pos.PD1.pos.A05.S17.comb                   T cells
#> cy80.Cd45.pos.PD1.pos.C02.S62.comb                NK/T cells
#> cy80.Cd45.pos.PD1.pos.D12.S96.comb           B cells/T cells
#>                                          most_probable_cell_type
#> cy80.Cd45.pos.PD1.pos.B09.S45.comb                       T cells
#> cy80.Cd45.pos.Pd1.neg.S366.H06.S366.comb                 B cells
#> cy80.Cd45.pos.Pd1.neg.S202.A10.S202.comb                 B cells
#> cy80.Cd45.pos.Pd1.neg.S201.A09.S201.comb                 B cells
#> cy80.Cd45.pos.Pd1.neg.S221.B05.S221.comb                 B cells
#> cy80.Cd45.pos.PD1.pos.A03.S15.comb                       B cells
#> cy80.Cd45.pos.PD1.pos.B11.S47.comb                       T cells
#> cy80.Cd45.pos.PD1.pos.S189.H09.S189.comb                 T cells
#> cy80.Cd45.pos.PD1.pos.A05.S17.comb                       T cells
#> cy80.Cd45.pos.PD1.pos.C02.S62.comb                       T cells
#> cy80.Cd45.pos.PD1.pos.D12.S96.comb                       B cells

New columns are:

Result visualization

The predicted cell types can now simply be visualized using the matching plotting functions. In this example, we use Seurat’s DimPlot function:

# Visualize the cell types
Seurat::DimPlot(seurat.obj, group.by = "most_probable_cell_type")

With the current number of cell classifiers, we identify cells belonging to 2 cell types (B cells and T cells) and to 2 subtypes of T cells (CD4+ T cells and CD8+ T cells). The other cells (red points) are not among the cell types that can be classified by the predefined classifiers. Hence, they have an empty label.

For a certain cell type, users can also view the prediction probability. Here we show an example of B cell prediction probability:

# Visualize the cell types
Seurat::FeaturePlot(seurat.obj, features = "B_cells_p")

Cells predicted to be B cells with higher probability have darker color, while the lighter color shows lower or even zero probability of a cell to be B cells. For B cell classifier, the threshold for prediction probability is currently at 0.5, which means cells having prediction probability at 0.5 or above will be predicted as B cells.

The automatic cell identification by scAnnotatR matches the traditional cell assignment, ie. the approach based on cell canonical marker expression. Taking a simple example, we use CD19 and CD20 (MS4A1) to identify B cells:

# Visualize the cell types
Seurat::FeaturePlot(seurat.obj, features = c("CD19", "MS4A1"), ncol = 2)

We see that the marker expression of B cells exactly overlaps the B cell prediction made by scAnnotatR.

Session Info

sessionInfo()
#> R version 4.2.0 RC (2022-04-19 r82224)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Mojave 10.14.6
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#> 
#> attached base packages:
#> [1] stats4    stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] scAnnotatR_1.2.0            SingleCellExperiment_1.18.0
#>  [3] SummarizedExperiment_1.26.0 Biobase_2.56.0             
#>  [5] GenomicRanges_1.48.0        GenomeInfoDb_1.32.0        
#>  [7] IRanges_2.30.0              S4Vectors_0.34.0           
#>  [9] BiocGenerics_0.42.0         MatrixGenerics_1.8.0       
#> [11] matrixStats_0.62.0          SeuratObject_4.0.4         
#> [13] Seurat_4.1.0               
#> 
#> loaded via a namespace (and not attached):
#>   [1] utf8_1.2.2                    reticulate_1.24              
#>   [3] tidyselect_1.1.2              RSQLite_2.2.12               
#>   [5] AnnotationDbi_1.58.0          htmlwidgets_1.5.4            
#>   [7] grid_4.2.0                    Rtsne_0.16                   
#>   [9] pROC_1.18.0                   munsell_0.5.0                
#>  [11] codetools_0.2-18              ica_1.0-2                    
#>  [13] future_1.25.0                 miniUI_0.1.1.1               
#>  [15] withr_2.5.0                   spatstat.random_2.2-0        
#>  [17] colorspace_2.0-3              filelock_1.0.2               
#>  [19] highr_0.9                     knitr_1.38                   
#>  [21] ROCR_1.0-11                   tensor_1.5                   
#>  [23] listenv_0.8.0                 labeling_0.4.2               
#>  [25] GenomeInfoDbData_1.2.8        polyclip_1.10-0              
#>  [27] farver_2.1.0                  bit64_4.0.5                  
#>  [29] parallelly_1.31.1             vctrs_0.4.1                  
#>  [31] generics_0.1.2                ipred_0.9-12                 
#>  [33] xfun_0.30                     BiocFileCache_2.4.0          
#>  [35] R6_2.5.1                      bitops_1.0-7                 
#>  [37] spatstat.utils_2.3-0          cachem_1.0.6                 
#>  [39] DelayedArray_0.22.0           assertthat_0.2.1             
#>  [41] promises_1.2.0.1              scales_1.2.0                 
#>  [43] nnet_7.3-17                   gtable_0.3.0                 
#>  [45] globals_0.14.0                goftest_1.2-3                
#>  [47] timeDate_3043.102             rlang_1.0.2                  
#>  [49] splines_4.2.0                 lazyeval_0.2.2               
#>  [51] ModelMetrics_1.2.2.2          spatstat.geom_2.4-0          
#>  [53] BiocManager_1.30.17           yaml_2.3.5                   
#>  [55] reshape2_1.4.4                abind_1.4-5                  
#>  [57] httpuv_1.6.5                  caret_6.0-92                 
#>  [59] tools_4.2.0                   lava_1.6.10                  
#>  [61] ggplot2_3.3.5                 ellipsis_0.3.2               
#>  [63] spatstat.core_2.4-2           jquerylib_0.1.4              
#>  [65] RColorBrewer_1.1-3            proxy_0.4-26                 
#>  [67] ggridges_0.5.3                Rcpp_1.0.8.3                 
#>  [69] plyr_1.8.7                    zlibbioc_1.42.0              
#>  [71] purrr_0.3.4                   RCurl_1.98-1.6               
#>  [73] rpart_4.1.16                  deldir_1.0-6                 
#>  [75] pbapply_1.5-0                 cowplot_1.1.1                
#>  [77] zoo_1.8-10                    ggrepel_0.9.1                
#>  [79] cluster_2.1.3                 magrittr_2.0.3               
#>  [81] data.table_1.14.2             scattermore_0.8              
#>  [83] lmtest_0.9-40                 RANN_2.6.1                   
#>  [85] fitdistrplus_1.1-8            patchwork_1.1.1              
#>  [87] mime_0.12                     evaluate_0.15                
#>  [89] xtable_1.8-4                  gridExtra_2.3                
#>  [91] compiler_4.2.0                tibble_3.1.6                 
#>  [93] KernSmooth_2.23-20            crayon_1.5.1                 
#>  [95] htmltools_0.5.2               mgcv_1.8-40                  
#>  [97] later_1.3.0                   tidyr_1.2.0                  
#>  [99] lubridate_1.8.0               DBI_1.1.2                    
#> [101] dbplyr_2.1.1                  MASS_7.3-57                  
#> [103] rappdirs_0.3.3                data.tree_1.0.0              
#> [105] Matrix_1.4-1                  cli_3.3.0                    
#> [107] parallel_4.2.0                gower_1.0.0                  
#> [109] igraph_1.3.1                  pkgconfig_2.0.3              
#> [111] plotly_4.10.0                 spatstat.sparse_2.1-1        
#> [113] recipes_0.2.0                 foreach_1.5.2                
#> [115] bslib_0.3.1                   hardhat_0.2.0                
#> [117] XVector_0.36.0                prodlim_2019.11.13           
#> [119] stringr_1.4.0                 digest_0.6.29                
#> [121] sctransform_0.3.3             RcppAnnoy_0.0.19             
#> [123] spatstat.data_2.2-0           Biostrings_2.64.0            
#> [125] rmarkdown_2.14                leiden_0.3.9                 
#> [127] uwot_0.1.11                   kernlab_0.9-30               
#> [129] curl_4.3.2                    shiny_1.7.1                  
#> [131] lifecycle_1.0.1               nlme_3.1-157                 
#> [133] jsonlite_1.8.0                viridisLite_0.4.0            
#> [135] fansi_1.0.3                   pillar_1.7.0                 
#> [137] lattice_0.20-45               KEGGREST_1.36.0              
#> [139] fastmap_1.1.0                 httr_1.4.2                   
#> [141] survival_3.3-1                interactiveDisplayBase_1.34.0
#> [143] glue_1.6.2                    png_0.1-7                    
#> [145] iterators_1.0.14              BiocVersion_3.15.2           
#> [147] bit_4.0.4                     class_7.3-20                 
#> [149] stringi_1.7.6                 sass_0.4.1                   
#> [151] blob_1.2.3                    AnnotationHub_3.4.0          
#> [153] memoise_2.0.1                 dplyr_1.0.8                  
#> [155] e1071_1.7-9                   irlba_2.3.5                  
#> [157] future.apply_1.9.0            ape_5.6-2